
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
						import torch

if is_vision_available():
						import PIL


SCREAMING_SNAKE_CASE :		Union[str, Any]       			=							logging.get_logger(__name__)



def 							lowerCamelCase_	( __UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase  ):
				def constraint_to_multiple_of(__UpperCamelCase			,    __UpperCamelCase			,    __UpperCamelCase=0			,    __UpperCamelCase=None  ):
								A_													=	round(val / multiple  ) * multiple

								if max_val is not None and x > max_val:
												A_													=	math.floor(val / multiple  ) * multiple

								if x < min_val:
												A_													=	math.ceil(val / multiple  ) * multiple

								return x

				A_													=	(output_size, output_size) if isinstance(a_			,    a_  ) else output_size

				A_													=	get_image_size(a_  )
				A_													=	output_size

				# determine new height and width
				A_													=	output_height / input_height
				A_													=	output_width / input_width

				if keep_aspect_ratio:
								# scale as little as possible
								if abs(1 - scale_width  ) < abs(1 - scale_height  ):
												# fit width
												A_													=	scale_width
								else:
												# fit height
												A_													=	scale_height

				A_													=	constraint_to_multiple_of(scale_height * input_height			,    multiple=a_  )
				A_													=	constraint_to_multiple_of(scale_width * input_width			,    multiple=a_  )

				return (new_height, new_width)






class        __lowercase		(       _A							):
					__magic_name__    :				int  						=						['''pixel_values''']


					def __init__(						self							,							a__ = True							,							a__ = None							,							a__ = PILImageResampling.BILINEAR							,							a__ = False							,							a__ = 1							,							a__ = True							,							a__ = 1 / 2_5_5							,							a__ = True							,							a__ = None							,							a__ = None							,							**a__							,							)		->				List[Any]:





									'''simple docstring'''
									super().__init__(**a_    )
									A_													=	size if size is not None else {"height": 3_8_4, "width": 3_8_4}
									A_													=	get_size_dict(a_    )
									A_													=	do_resize
									A_													=	size
									A_													=	keep_aspect_ratio
									A_													=	ensure_multiple_of
									A_													=	resample
									A_													=	do_rescale
									A_													=	rescale_factor
									A_													=	do_normalize
									A_													=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									A_													=	image_std if image_std is not None else IMAGENET_STANDARD_STD


					def 							lowerCAmelCase_     (						self							,							a__							,							a__							,							a__ = False							,							a__ = 1							,							a__ = PILImageResampling.BICUBIC							,							a__ = None							,							**a__							,							)		->				Tuple:





									'''simple docstring'''
									A_													=	get_size_dict(a_    )
									if "height" not in size or "width" not in size:
													raise ValueError(F"The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}"    )
									A_													=	get_resize_output_image_size(
									    a_							,							output_size=(size['''height'''], size['''width'''])							,							keep_aspect_ratio=a_							,							multiple=a_							,							)
									return resize(a_							,							size=a_							,							resample=a_							,							data_format=a_							,							**a_    )


					def 							lowerCAmelCase_     (						self							,							a__							,							a__							,							a__ = None							,							**a__							,							)		->				List[Any]:





									'''simple docstring'''
									return rescale(a_							,							scale=a_							,							data_format=a_							,							**a_    )


					def 							lowerCAmelCase_     (						self							,							a__							,							a__							,							a__							,							a__ = None							,							**a__							,							)		->				Dict:





									'''simple docstring'''
									return normalize(a_							,							mean=a_							,							std=a_							,							data_format=a_							,							**a_    )


					def 							lowerCAmelCase_     (						self							,							a__							,							a__ = None							,							a__ = None							,							a__ = None							,							a__ = None							,							a__ = None							,							a__ = None							,							a__ = None							,							a__ = None							,							a__ = None							,							a__ = None							,							a__ = None							,							a__ = ChannelDimension.FIRST							,							**a__							,							)		->				int:





									'''simple docstring'''
									A_													=	do_resize if do_resize is not None else self.do_resize
									A_													=	size if size is not None else self.size
									A_													=	get_size_dict(a_    )
									A_													=	keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
									A_													=	ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
									A_													=	resample if resample is not None else self.resample
									A_													=	do_rescale if do_rescale is not None else self.do_rescale
									A_													=	rescale_factor if rescale_factor is not None else self.rescale_factor
									A_													=	do_normalize if do_normalize is not None else self.do_normalize
									A_													=	image_mean if image_mean is not None else self.image_mean
									A_													=	image_std if image_std is not None else self.image_std

									A_													=	make_list_of_images(a_    )

									if not valid_images(a_    ):
													raise ValueError(
													    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
													    '''torch.Tensor, tf.Tensor or jax.ndarray.'''    )

									if do_resize and size is None or resample is None:
													raise ValueError('''Size and resample must be specified if do_resize is True.'''    )

									if do_rescale and rescale_factor is None:
													raise ValueError('''Rescale factor must be specified if do_rescale is True.'''    )

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('''Image mean and std must be specified if do_normalize is True.'''    )

									# All transformations expect numpy arrays.
									A_													=	[to_numpy_array(a_    ) for image in images]

									if do_resize:
													A_													=	[self.resize(image=a_							,							size=a_							,							resample=a_    ) for image in images]

									if do_rescale:
													A_													=	[self.rescale(image=a_							,							scale=a_    ) for image in images]

									if do_normalize:
													A_													=	[self.normalize(image=a_							,							mean=a_							,							std=a_    ) for image in images]

									A_													=	[to_channel_dimension_format(a_							,							a_    ) for image in images]

									A_													=	{"pixel_values": images}
									return BatchFeature(data=a_							,							tensor_type=a_    )


					def 							lowerCAmelCase_     (						self							,							a__							,							a__ = None    )		->				Tuple:





									'''simple docstring'''
									A_													=	outputs.logits

									# Resize logits and compute semantic segmentation maps
									if target_sizes is not None:
													if len(a_    ) != len(a_    ):
																	raise ValueError(
																	    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''    )

													if is_torch_tensor(a_    ):
																	A_													=	target_sizes.numpy()

													A_													=	[]

													for idx in range(len(a_    )    ):
																	A_													=	torch.nn.functional.interpolate(
																	    logits[idx].unsqueeze(dim=0    )							,							size=target_sizes[idx]							,							mode='''bilinear'''							,							align_corners=a_    )
																	A_													=	resized_logits[0].argmax(dim=0    )
																	semantic_segmentation.append(a_    )
									else:
													A_													=	logits.argmax(dim=1    )
													A_													=	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]    )]

									return semantic_segmentation






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase									=   {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



